A novel recursive modal parameter estimator for operational time-varying structural dynamic systems based on least squares support vector machine and time series model

Modal parameters are practically important for vibration control, structural dynamic design, health monitoring, etc. Presently, the commonly used recursive modal parameter estimators are generally based on the empirical risk minimization principle, and thus can result in overfitting problem easily. This paper presents a novel recursive modal parameter estimator for operational linear time-varying structures based on least squares support vector machine (LSSVM) and vector time-dependent autoregressive moving average model. A sliding-window forgetting mechanism is adapted to fix computational complexity of each update step and enhance tracking capability of the proposed estimator. A numerical example and a laboratory experiment are performed to demonstrate that the proposed structural risk minimization principle based estimator is robust to model structure and its computational complexities are independent of the dimension of output measurements comparing with the existing recursive extended least squares estimator.